• Journal of Environmental Science International
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• v.26 no.5
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• pp.621-638
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• 2017

Meteorological characteristics related to variations in ozone ($O_3$) concentrations in the Korean peninsula before, during, and after Typhoon Talas (1112) were analyzed using both observation data and numerical modeling. This case study takes into account a high $O_3$ episode (e.g., a daily maximum of ${\geq}90ppb$) without rainfall. Before the typhoon period, high $O_3$ concentrations in the study areas (e.g., Daejeon, Daegu, and Busan) resulted from the combined effects of stable atmospheric conditions with high temperature under a migratory anticyclone (including subsiding air), and wind convergence due to a change in direction caused by the typhoon. The $O_3$ concentrations during the typhoon period decreased around the study area due to very weak photochemical activity under increased cloud cover and active vertical dispersion under a low pressure system. However, the maximum $O_3$ concentrations during this period were somewhat high (similar to those in the normal period extraneous to the typhoon), possibly because of the relatively slow photochemical loss of $O_3$ by a $H_2O+O(^1D)$ reaction resulting from the low air temperature and low relative humidity. The lowest $O_3$ concentrations during the typhoon period were relatively high compared to the period before and after the typhoon, mainly due to the transport effect resulting from the strong nocturnal winds caused by the typhoon. In addition, the $O_3$ increase observed at night in Daegu and Busan was primarily caused by local wind conditions (e.g., mountain winds) and atmospheric stagnation in the wind convergence zone around inland mountains and valleys.

• Journal of Climate Change Research
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• v.6 no.3
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• pp.209-221
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• 2015

Temperature is one of the most important factors affecting crop growth. The diurnal cycle of the scale factor [Tsc] for air temperature and the surface temperature of crop leaf and soil could be estimated by the following equation : $[Tsc]=0.5{\times}sin(X+C)+0.5$. The daily air temperature (E[Ti]) according to the E&E time [X] can be estimated by following equation using average (Tavg), maximum (Tm) and minimum (Tn) temperature : $E[Ti]=Tn+(Tm-Tn){\times}[0.5{\times}sin\;\{X+(9.646Tavg+703.65)\}+0.5]$. The crop leaf temperature in 24th June 2014 was high as the order of red pepper without mulching > red pepper with mulching > soybean under drought > soybean with irrigation > Chinese cabbage. The case in estimating crop leaf surface temperature using air temperature and soil surface temperature was lower in the deviation compared to the case using air temperature for Chinese cabbage and red pepper. These results can be utilized for the crop models as input data with estimation.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.311-311
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• 2017

The relationships between rice grain filling and air temperature of maturing period in paddy field were analyzed to evaluate the effects of climatic change on rice productivity in Korea. Data of air temperature of 31, 41 and 51 days after heading(DAH) for 11 years from 2002 to 2016 were collected and analyzed to determine the effects on rice yield and yield component related traits of Chucheogbyeo, popular cultivar in Gyeonggi province in Korea. As the results, ripening ratio was closely correlated with the mean of daily maximum temperature (DMAT $r=0.71^*$), the mean of daily temperature difference (DTD, $r=0.67^*$) of 41 DAH and DTD ($0.65^*$) of 51 DAH. Weight of 1,000 paddy rice grains was closely correlated with accumulated sunshine hours (ASH) of 31 ($r=0.84^{**}$), 41 ($r=0.75^{**}$), 51 ($r=0.72^*$) DAH. Brown rice grain weight recovery ratio was closely correlated with DTD ($r=0.76^{**}$) and ASH ($r=0.84^{**}$) of 31 DAH, DMAT ($r=0.75^{**}$, $r=0.79^{**}$), DTD ($r=0.79^{**}$, $r=0.77^{**}$) and ASH ($r=0.81^{**}$, $r=0.79^{**}$) of 41 and 51 DAH. Paddy rice yield was closely correlated with MDT ($r=-0.63^*$) of 31 DAH, mean of daily minimum temperature (DMIT, $r=-0.83^{**}$, $r=-0.70^*$), DTD ($r=0.71^*$, $r=0.62^*$) of 31 and 41 DAH. Brown rice yield was correlated closely with DMIT ($r=-0.86^{**}$, $r=-0.73^*$) and DTD ($r=0.80^{**}$, $r=0.72^*$) of 31, 41 DAH, and DTD ($r=0.69^*$) of 51 DAH. Milled rice yield was correlated closely with DMIT ($r=-86^{**}$, $r=-0.73^*$), DTD ($r=0.79^{**}$, $r=0.71^*$) of 31, 41 DAH, and DTD ($r=0.68^*$) of 51 DAH.

• Korean Journal of Medicinal Crop Science
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• v.28 no.6
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• pp.463-470
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• 2020

Background: Evaluation of transpiration is required for agricultural and environmental management applications, as crop yields and plant growth are primarily water limited. This study aimed to determine the transpiration and carbon accumulation of Cnidium officinale. Methods and Results: The transpiration of C. officinale was evaluated using weighing lysimeter. The relationship between transpiration and factors such as solar radiation, air temperature, and leaf area was assessed. Transpiration increased as the leaf area increased with the growth stage. Furthermore, daily transpiration per unit leaf area was 0.69 ± 0.16 g·cm-2·day-1 and there were no significant differences in daily transpiration during the cultivation period. The maximum transpiration was 620.6 g m-2·h-1 and diurnal changes in transpiration were highly correlated with solar radiation although the maximum transpiration was observed at the air temperatures of 20℃ - 26℃. The ratio of carbon accumulation to transpiration was 0.12%. Conclusions: Our results indicated that the transpiration of C. officinale is primarily regulated by solar radiation energy on clear days and that 97% of the water is discharged through transpiration for heat dissipation. Therefore, weighing lysimeters can measure transpiration accurately and may be useful in interpreting plant growth.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.20 no.2
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• pp.128-136
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• 2008

It is necessary to estimate the cooling load of the next day for effective control of ice thermal storage system. In this paper, new methodology is proposed to estimate the cooling load using design parameters of building and predicted weather data. Only six input parameters such as sensible heat coefficient and constant, latent heat coefficient and constant, maximum and minimum temperature are necessary to obtain hourly distribution of cooling load for the next day. Two benchmarking buildings(hospital and research institute) are selected to validate the performance of the proposed method, and the estimated cooling loads in hourly and daily bases are calculated and compared with the measured data for E hospital. The estimated results show fairly good agreement with the measured data for both buildings.

• Korean Journal of Agricultural and Forest Meteorology
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• v.3 no.4
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• pp.199-205
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• 2001

This study was conducted to figure out temperature profiles of a partially developed paddy rice canopy, which are necessary to run plant disease forecasting models. Air temperature over and within the developing rice canopy was monitored from one month after transplanting (June 29) to just before heading (August 24) in 1999 and 2001. During the study period, the temporal march of the within-canopy profile was analyzed and an empirical formula was developed for simulating the profile. A partially developed rice canopy temperature seemed to be controlled mainly by the ambient temperature above the canopy and the water temperature beneath the canopy, and to some extent by the solar altitude, resulting in alternating isothermal and inversion structures. On sunny days, air temperature at the height of maximum leafages was increased at the same rate as the ambient temperature above the canopy after sunrise. Below the height, the temperature increase was delayed until the solar noon. Air temperature near the water surface varied much less than those of the outer- and the upper-canopy, which kept increasing by the time of daily maximum temperature observed at the nearby synoptic station. After sunset, cooling rate is much less at the lower canopy, resulting in an isothermal profile at around the midnight. A fairly consistent drop in temperature at rice paddies compared with the nearby synoptic weather stations across geographic areas and time of day was found. According to this result, a cooling by 0.6 to 1.2$^{\circ}C$ is expected over paddy rice fields compared with the officially reported temperature during the summer months. An empirical equation for simulating the temperature profile was formulated from the field observations. Given the temperature estimates at 150 cm above the canopy and the maximum deviation at the lowest layer, air temperature at any height within the canopy can be predicted by this equation. As an application, temperature surfaces at several heights within rice fields were produced over the southwestern plains in Korea at a 1 km by 1km grid spacing, where rice paddies were identified by a satellite image analysis. The outer canopy temperature was prepared by a lapse rate corrected spatial interpolation of the synoptic temperature observations combined with the hourly cooling rate over the rice paddies.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.73-79
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• 2019

Continuous and tremendous data (canopy temperature and meteorological variables) are necessary to determine Crop Water Stress Index (CWSI). This study investigated the optimal monitoring time and interval of canopy temperature and meteorological variables (air temperature, relative humidity, solar radiation and wind speed) to determine CWSIs. The Nash-Sutcliffe model efficiency coefficient (NSE) was used to quantitatively describe the accuracy of sampling method depending upon various time intervals (t=5, 10, 15, 20, 30 and 60 minutes) and CWSIs per every minute were used as a reference. The NSE coefficient of wind speed was 0.516 at the sampling time of 60 minutes, while the ones of other meteorological variables and canopy temperature were greater than 0.8. The pattern of daily CWSIs increased from 8:00 am, reached the maximum value at 12:00 pm, then decreased after 2:00 pm. The statistical analysis showed that the data collection at 11:40 am produced the closest CWSI value to the daily average of CWSI, which indicates that just one time of measurement could be representative throughout the day. Overall, the findings of this study contributes to the economical and convenient method of quantifying CWSIs and irrigation management.

• Journal of Environmental Science International
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• v.22 no.9
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• pp.1213-1226
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• 2013

$PM_{10}$ concentration is related to the meteorological variables including to local and synoptic meteorology. In this study the $PM_{10}$ concentrations of Busan in 2007~2011 were analyzed and the days of yellow sand or rainfall which is more than 5 mm were excluded. The sections of $PM_{10}$ concentration were divided according to 10-quantiles, quartiles and 90-quantiles. The 90-quantiles of daily $PM_{10}$ concentration were selected as high concentration dates. In the high concentration dates the daily mean averaged cloudness, mean daily surface wind speed, daily mean surface pressure and PBL height were low and diurnal variation of surface pressure and daily maximum surface temperature were high. When the high $PM_{10}$ dates occurred, the west and south wind blew on the ground and the west wind blew strongly on the 850 hPa. So it seemed that long range transboundary air pollutants made effects on the high concentration dates. The cluster analysis using Hysplit model which is the backward trajectory was made on the high concentration dates. As a result, 3 clusters were extracted and on the short range transboundary cluster the daily mean relative humidity and cloudness were high and PBL height was low.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.42 no.4
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• pp.473-482
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• 1997

Temperature, humidity and wetness duration were monitored for fully developed paddy rice canopies with 3 different structures induced by the seeding method(puddled-soil drill seeding, DS ; hand broadcasting, HB ; machine broadcasting, MB). Within-canopy air temperature averaged over "clear sky" hours during the study period(maximum tillering through heading) was lower than the screen temperature at a nearby standard weather station, especially in the night. The same trend was true for "overcast sky" hours except the diurnal distinction. Vapor pressure within the canopy was high during the daytime and low in the night, making the daytime deviation from outside the canopy more significant on clear days. Under the overcast sky, the canopy maintained a steady 5 to 10％ higher vapor pressure than the outside regardless of day or night. Daily maximum temperature was observed to be higher within the canopies with more leaf mass, making MB the highest, HB the lowest, and DS in between. Relative humidity was over 90％ in the night and dropped to 70％ in the mid-afternoon, but vapor pressure within the canopy was highest at around 13:00 LST. Dew point depression was lowest and, combined with the temperature, the relative humidity was highest in HB. Mean period of wetting duration was in the order of DS＞HB＞MB, while the dew point depression was greatest in DS.

• 한국해양학회지
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• v.5 no.1
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• pp.6-13
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• 1970

Ten days and monthly mean temperatures were analysed daily data observed during July, 1916 to March, 1970 statistically. Periodic characters were calculated by Δn, new method of approximate solution of Schuster Method. According to ten days mean temperatures, annual variation function is F($\theta_d$)=16.29-5.27 cos $\theta_d$+0.75 cos2 $\theta_d$-3.14 sin $\theta_d$+1.16 sin2 $\theta_d$-0.63 sin $\3{theta}_d$, where $\theta_d$=$-\frac{\pi}{18}$(d-3), d is the order of ten days period, 1 to 36. Annual mean water temperature is 16.3$^{\circ}C$, minimum in the last ten days of February 10.9$^{\circ}C$, maximum in the last ten days of August 24.5$^{\circ}C$. Periodic character of secular variation shows 11 year and its curve is F($\theta_y$)=16.29+0.53 cos $\theta_y$ -0.16cos $2{\theta}_y$+0.10 cos$3{\theta}_y$-0.10 sin $\theta_y$, where $\theta_y$=2$-\frac{2\pi}{11}$(y-1920), y is calendar year. And the relation between air temperature x and water temprature y is following. y=9.67 1.035$\^x$